Method, system and storage medium for optimizing a product design

ABSTRACT

An exemplary embodiment of the invention is a method for optimizing a product design. The method includes specifying a plurality of application parameters for the product. A plurality of predetermined factors and responses are obtained in response to the plurality of application parameters. A transfer function is obtained which relates at least one factor to at least one response. The transfer function is optimized in response to user-defined optimization criteria to generate an optimized factor and an optimized response. The optimized factor and the optimized response are then displayed.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. provisional patentapplication No. 60/162,447 filed Oct. 29, 1999, the entire contents ofwhich are incorporated herein by reference.

BACKGROUND OF THE INVENTION

The invention relates to a method and system for evaluating a productdesign. The task of generating, evaluating and implementing a productdesign is a formidable one. Typically, product designs are generated bydesign personnel and put through a process often referred to as designreview. In design review, individuals skilled in design, production,inspection, packaging, etc. evaluate designs. This often leads tore-design and further design review cycles delaying new productintroduction. Once a product design is selected, prototypes may beproduced using different materials and/or manufacturing processes.Although the selection of materials and manufacturing processes isperformed by those skilled in the art, this process is still aniterative trial and error process that often results in changes to thedesign accompanied by additional prototyping. This cycle delays newproduct introduction and is often focused on internal metrics ratherthan customer metrics.

A product design may be represented by product factors (e.g., material,processing parameters, dimensions) that affect product responses (e.g.,cost, performance). The factors and responses define a design space.Much of the above-described iterative cycle conventionally performed inthe art is an attempt to locate a region in the design space in whichproduct factors and product responses are within desired limits orconstraints. While locating a region in a design space where designcriteria are met is helpful, there may exist an optimum point in thedesign space where responses are optimized thus enhancing the product.Thus, there is a need in the art for a system that improves designs byallowing a designer to optimize responses.

BRIEF SUMMARY OF THE INVENTION

An exemplary embodiment of the invention is a method for optimizing aproduct design. The method includes specifying a plurality ofapplication parameters for the product. A plurality of predeterminedfactors and responses are obtained in response to the plurality ofapplication parameters. A transfer function is obtained which relates atleast one factor to at least one response. The transfer function isoptimized in response to user-defined optimization criteria to generatean optimized factor and an optimized response. The optimized factor andthe optimized response are then displayed. Also disclosed are a systemand storage medium for implementing the method.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flowchart of a process for designing a product in anexemplary embodiment of the invention;

FIG. 2 is a block diagram of a system for designing a product;

FIG. 3 depicts an exemplary interface to an engineering designcalculator;

FIG. 4 depicts an exemplary interface for entering application factors;

FIG. 5 depicts an exemplary interface for selecting materials;

FIG. 6 depicts an exemplary interface for entering responses;

FIG. 7 depicts an exemplary interface for entering manufacturingfactors;

FIGS. 8A and 8B, an exemplary factor/response summary;

FIG. 9 depicts an exemplary interface with a DOE module;

FIG. 10 depicts exemplary design of experiments data;

FIG. 11 depicts an exemplary interface for optimization;

FIG. 12 depicts an exemplary interface for setting up a visualization;

FIG. 13 depicts an exemplary visualization for two materials;

FIG. 14 is a flowchart of a process for designing a product in analternate embodiment of the invention;

FIG. 15 is a block diagram of an alternate system for designing aproduct; and,

FIG. 16 depicts an exemplary interface to media application module.

DETAILED DESCRIPTION OF THE INVENTION

An exemplary embodiment of the invention is a method and system fordesigning a product. As used herein, product is intended to have a broadmeaning encompassing a variety of items. Specific examples of productdesigns are provided, but do not limit the scope of the invention. FIG.1 is a flowchart of a process for designing a product and FIG. 2 is ablock diagram of a product design system shown generally at 10. As theuser goes through the process shown in FIG. 1, parts of the productdesign system 10 are utilized as described herein. As shown in FIG. 2,the product design system 10 includes a number of modules for performingcertain functions during the design process.

Shown in FIG. 2 are a quality function deployment (QFD) module 12, anengineering design calculator 14, a design of experiments (DOE) module16, a regression module 20, an optimization module 22 and avisualization module 24. Each module may be implemented through asoftware application implemented by a general purpose computer. Themodules may be implemented on a single general purpose computer andaccessed by the user through a user interface 26. Alternatively, themodules may be implemented on a plurality of general purpose computersremotely located from each other. The user interface 26 may access thevarious modules over a network 27 such as a local area network (LAN),wide area network (WAN), global network (e.g., Internet), etc. Themodules may be implemented on computers which act as servers formultiple client computers. The user interface may include a userinterface application (e.g., web browser) or interfacing with one ormore servers that execute software applications corresponding to themodules shown in FIG. 2.

Referring to FIG. 1, the process for designing a product will now bedescribed. The process begins at step 30 where the user selects adesired task such as quality function deployment (QFD) at step 32,engineering calculations at step 34 or use of a design for six sigma(DFSS) toolset at step 36. If the user selects QFD at step 32, the QFDmodule 12 is accessed. The QFD module 12 allows the user to perform aquality function deployment process in which process variables orproduct design parameters (often referred to as key control parameters(KCPs) or factors) are analyzed to determine effects on critical toquality parameters (CTQs) or responses. The use can define CTQs anddetermine the effect that KCPs have on CTQs. Conventional QFDapplications software may be used to allow the user to define CTQs andanalyze the interaction between KCPs and the CTQs.

If the user selects engineering calculations at step 34, the engineeringdesign calculator 14 is accessed. The engineering design calculator 14allows the user to execute calculations for a single set of conditions.FIG. 3 depicts an exemplary interface to the engineering designcalculator 14 which is directed to performing calculations related tomolding of plastic components. The engineering design calculator 14allows the user to select material through a select material icon 40.This connects the user to a database of plastics which includesparameters of the plastics such as cost, hardness, etc. The user canselect different materials to view the effect that different materialshave on certain responses or Y's. The user can also select a geometryfor the molded plastic component as shown at geometry selection option42.

The user then enters values 45 for factors 44 (or X's) related to theplastic component and the molding process. The values 45 are then usedto compute responses 46 (or Y's) which provide information such as cycletime and cost to the user. The calculations which derive the responses46 from the factors 44 are based on predetermined functions. Theengineering design calculator 14 performs calculations based on a singleset of factors 44. Thus, for the user to see the effect of a change in afactor 44 (e.g., mold temperature) on a response 46 (e.g., total cost),the user must change the value 45 of a factor 44 and recalculate theresponses 46. Thus, the engineering design calculator is used togenerally determine the effect of factors 44 on responses 46, but morerobust tools are used, as described herein, to optimize one or moreresponses 46.

If the user selects DFSS toolset at step 36, the process flows to step48 where the user enters application factors concerning the product tobe manufactured. The application factors define the product to bemanufactured and generally will not vary with materials or processingparameters. FIG. 4 depicts an exemplary user interface for entering theapplication factors. As shown in FIG. 4, the user can select a geometryat geometry selection area 70 and can specify values 73 for applicationfactors 72. The application factors shown in FIG. 4 are directed to aplastic part. It is understood that other types application factors maybe used given the application and the invention is not limited toplastic components.

At the application factor entry step 48, the user can also enterstatistical data in addition to the value 73 for each application factor72. As shown in FIG. 4, the user can enter a standard deviation 74, alow limit 76 and a high limit 78 for each application factor. One ormore of the statistical data may be used in the design of experimentsprocess described herein. The user can specify that an applicationfactor 72 be used in a design of experiments (DOE) by checking a designof experiments indicator 80. Typically, the user enters a low limit 76and/or a high limit 78 if an application factor is to be used in adesign of experiments. The application factors 72 may also include oneor more user-defined application factors 82. Several of the applicationfactors 72 are predefined. The user-defined application factors 82 allowthe user to enter an application factor that is not provided for in thepredetermined application factors and have this user-defined applicationfactor 82 considered in a subsequent design of experiments.

Once the application factors 72 have been entered, flow proceeds to step50 where the user selects a material to be used in forming the product.FIG. 5 is an exemplary interface for selecting materials. The user canidentify a material through a select material icon 86 which may directthe user to a database of commercially available materials. If the userselects a commercially available material, the material characteristics(cost, hardness, melt temperature, etc.) are contained in the databaseand are accessible during later stages of the design process. Theengineering design calculator 14, described above, may be used to helpthe user select appropriate materials for a particular application byproviding responses 46 for a given material. Instead of selecting apredefined material, the user may define characteristics of a materialthat is not commercially available. For example, the user may define acustom material by entering material characteristics (cost, hardness,etc.) that are not realized by any commercially available material. Thisallows the user to design a product based on non-existing materials andevaluate whether the expense in generating the custom material iswarranted.

Once the user has selected a material, either predefined oruser-defined, at step 50, flow proceeds to step 52 where the user entersresponses. FIG. 6 is an exemplary interface for entering responses 90.The responses 90 represent parameters that the user may want to controlor optimize. For each response, the user can enter statistical dataincluding a low limit 92, a target value 94 and a high limit 96. The lowlimit 92, target value 94 and/or high limit 96 may all be used in thedesign of experiments process described herein. The user can also definea type of optimization to be performed on a response 90 through anoptimization indicator 98. As described herein, the system can determinefactors so that one or more responses are optimized. The optimizationindicator 98 allows the user to define the type of optimization (e.g.,minimize, maximize, meet a target value, etc.). The user can designatethat a response 90 be used in a subsequent design of experiments processby selecting a design of experiments indicator 100. The responses 90 mayalso include one or more user-defined responses 102. Several of theresponses 90 are predefined. The user-defined responses 102 allow theuser to enter a response that is not provided for in the predeterminedresponses and have this user-defined response 102 considered in thedesign of experiments and optimization steps described herein. Theresponses shown in FIG. 6 are directed to a molding a plastic part. Itis understood that other types of responses may be used given theapplication and the invention is not limited to plastic components.

Once the user has defined responses 90, predefined and/or user-defined,at step 52, flow proceeds to step 54 where the user enters manufacturingfactors. FIG. 7 depicts an exemplary user interface for entering themanufacturing factors 108. The manufacturing factors 108 representfactors in the manufacturing process that may be controlled or modified.The user can specify a value 109 for manufacturing factors 108. The usercan also enter statistical data in addition to the value 109 for eachmanufacturing factor 108. As shown in FIG. 7, the user can enter astandard deviation 110, a low limit 112 and a high limit 114 for eachmanufacturing factor 108. One more of the statistical data may be usedin the design of experiments process described herein. The user canspecify that a manufacturing factor 108 be used in a design ofexperiments (DOE) by checking a design of experiments indicator 116.Typically, the user enters a low limit 112 and/or a high limit 114 if amanufacturing factor is to be used in a design of experiments. Themanufacturing factors 108 may also include one or more user-definedmanufacturing factors 118. Several of the manufacturing factors 108 arepredefined. The user-defined manufacturing factors 118 allow the user toenter a manufacturing factor that is not provided for in thepredetermined manufacturing factors and have this user-definedmanufacturing factor 118 considered in a subsequent design ofexperiments. The manufacturing factors shown in FIG. 7 are directed to aplastic molding process. It is understood that other types manufacturingfactors may be used given the application and the invention is notlimited to manufacturing of plastic components.

Once the manufacturing factors, predefined and/or user-defined, havebeen entered at step 54, flow proceeds to step 56 where the user ispresented with a factor/response summary such as that shown in FIGS. 8Aand 8B. As shown in FIG. 8, the factor/response summary includesapplication factors 72, user-defined application factors 82,manufacturing factors 108 and user-defined manufacturing factors 118. Inaddition, miscellaneous or other factors 122 may also be included whichdo not correspond to the categories of application factors, user-definedapplication factors, manufacturing factors and user-definedmanufacturing factors. The term factors, as used herein, is intended tohave a broad meaning and is not limited to the particular examples orcategories described above. Instead of progressing through steps 48, 50,52 and 54, a user may proceed directly to step 56 and enter factors andresponses as described above. Steps 48, 50, 52 and 54 are directed to alimited set of factors or responses and may help focus the user onspecific aspects of the application. An experienced user, for example,may proceed directly to step 56 and enter factors.

The ability to enter user-defined application factors, user-definedmaterials, user-defined responses and user-defined manufacturing factorsallows the system 10 to simulate manufacturing of products based, inpart, on hypothetical, user-defined data. The factors, materials andresponses, and their interrelationships may be defined based on existingsimulation designs, empirical data, scientific analysis (e.g.,thermodynamics, physics) and hypothetical, user-defined data. Thisprovides a powerful tool for the designer in that user-defined data canbe entered along with established data. The design of experiments,transfer function generation and optimization, described herein, isperformed in response to the user-defined data.

The factor/response summary also includes responses 90 and user-definedresponses 102. As shown in FIGS. 8A and 8B, a value 126 may becalculated for responses 90 and user-defined responses 102. Thecalculations are performed by the engineering design calculator 14. Thisprovides the user with a general indication of how factor values effectresponse values. If the user wants to determined how changes in a factoreffect a response, the user must alter the value of a factor andinstruct the engineering design calculator to recalculate the responses.The user may view the factor/response summary and determine that certainresponses (e.g., total cost) are too far from desired values and returnto prior steps, such as material selection to effect the response. Tooptimize responses, more sophisticated tools are used as describedherein.

Once the user is satisfied with the factor/response summary provided instep 56, flow proceeds to step 58 where the design of experimentsroutine is initiated. FIG. 9 depicts an exemplary user interface withthe DOE module 16 for initiating a design of experiments. The DOE module16 is a design of experiments software application as described above.The DOE module 16 may be implemented using commercially available designof experiments software applications. As shown in FIG. 9, the user setsup the design of experiments by selecting a DOE type through DOE typeicons 130. The user can select a default DOE, launch a DOE advisor tohelp select the appropriate DOE or specify a custom DOE. The user isalso presented with an identification of the materials 132, factors 134and responses 136 that are to be considered in the design of experimentsas selected by the user through DOE indicators.

Once the design of experiments has been setup in step 58, flow proceedsto step 60 where the design of experiments data is generated. The DOEmodule 16 performs the design of experiments process to generate designof experiments data. FIG. 10 depicts exemplary design of experimentsdata. For each material 132, the design of experiments module 16perturbs the factors 134 to assume values within a range defined by alow limit and a high limit and obtains values for responses 136. The lowlimit and high limit may be taken from the appropriate applicationfactors or the manufacturing factors entered by the user through steps48 and 54, respectively. Design of experiments data is generated foreach material 132 identified in the DOE setup step 58. For eachmaterial, a design space is generated corresponding to the relationshipbetween factors and responses.

To perform the DOE and compute the values for responses 136, the usercan select a Perform DOE icon 137. This initiates the DOE process inwhich values are determined for each response 136. The user can alsoselect a portion of the DOE data for computation of values by selectingthe Perform Area icon 139. The user can then select a subset of the DOEdata (e.g., lines 1-3) and determined values for responses 136 for onlythis subset of DOE data. The DOE module determines the values forresponses 136 by calling one or more other application modules. Forexample, the Melt Pressure to Fill may be calculated by an engineeringdesign module 17 (e.g., software application) that is initiated by theDOE module 16. The engineering design module 17 returns the value forMelt Pressure to Fill and this value is added to the DOE data. The TotalCycle Time may be derived by another software module such as a moldingsimulation module 19. The modules used to derive values for responses136 may have access to all the factors provided by the user. The modulescalled by the DOE module 16 to obtain values for responses can beestablished by the user or a system administrator. Alternatively,certain DOE responses 136 are determined by experimental data and thus,the user must enter the responses 136 based on experimental data.

Once the design of experiments process is completed, flow proceeds tostep 62 where one or more transfer functions are generated whichmathematically relate the factors 134 to responses 136 for each material132. The regression module 20 performs regression on the design ofexperiments data to generate the transfer functions which mathematicallyrelate the factors 132 to the responses 136 for each material. Thetransfer functions may be stored in a transfer function database 21 foruse in subsequent applications.

Once the transfer functions are generated, flow proceeds to step 64where optimization is performed. Optimization is performed byoptimization module 22. The user defines the type of optimizationthrough a user interface such as that shown in FIG. 11. For a givenmaterial 132, the user can optimize one or more responses 136 inmultiple ways using an optimization indicator 98. In addition, the usercan enter low limit 92, target value 94, high limit 96 as describedabove with respect to FIG. 6. These values may be carried over from step52 where the responses 136 were identified by the user or modified bythe user. For example, as shown in FIG. 11, the user has indicated thatthe Melt Pressure to Fill to be minimized, the Cycle Time be apredetermined target value and the Total Cost be minimized. Theoptimization module 22 uses the transfer functions generated by theregression module 20 and determines the appropriate values for factors134 to optimize the responses 136 as identified by the user. Inaddition, the optimization module 22 can determine statistical factorssuch as defects per million opportunity (DPMO) 150. A defect occurs whena response value exceeds an upper or lower limit. The DPMO value can beused to generate a Zst value which is commonly used in the six sigmadesign process to evaluate designs. Based on normal distributions, aDPMO value of 3.4 equals a Zst score of 6 meaning that the design meetsthe six sigma quality standards.

Additional constraints 152 on the optimization can entered which willimpose further limits on the optimization beyond those defined byoptimization indicators 98. For example, the user may specify that theproduct of Mold Temperature and Melt Pressure to Fill be less than apredetermined value. The user enters this constraint in the additionalconstraints field 152 by entering a mathematical representation of theconstraint and selecting a optimize indicator 154. The constraint servesas a boundary in the design space preventing the optimization modulefrom producing a solution that violates the constraint.

Additional optimization may be performed through the other optimizationfield 160. The optimization performed on responses 136 assumes that allthree responses are equally important to the user. The otheroptimization field 160 allows the user to assign a weight to one or moreresponses 136 to generate a global transfer function and to performoptimization on the global transfer function. For example, if MeltPressure to Fill (meltP) was three times more critical than Cycle Time(tcycle) and Total Cost (totalCost), the user may enter the followingrelationship in the other optimization field 160.

Y=3(meltP)+tcycle+totalCost.

The meltP response has been modified by a weight (e.g., 3) to reflectits importance. The optimization module 22 can then optimize on thevariable Y. The user requests this global optimization by defining theglobal transfer function in the other optimization field 160 andselecting an optimization indicator 161.

Once the factors 134 have been optimized based on the optimizationcriteria identified by the user, flow proceeds to step 66 where the usercan setup visualization of the factors 134 and responses 136 for eachmaterial 132. FIG. 12 depicts an exemplary user interface for setting upthe visualization. The user can select the materials 132, factors 134and responses 136 which are to be displayed and select the type ofdisplay through a visualization identifier 140. FIG. 13 depicts anexemplary visualization for two materials 132. Each of the responses 136is plotted against each factor 134 for each material. Since twomaterials were specified in the visualization setup in FIG. 12, twoplots are presented on each graph. The user can select the activematerial through a drop down menu 133 and the active material (i.e., thematerial for which the optimization points are shown) is distinguishedfrom other materials (e.g., the active material is shown with a thickline or a different color). Each graph also includes the optimizationdata entered by the user in the optimization step 64. For example, asshown in the plot of Melt Pressure to Fill (meltP) versus MeltTemperature (meltTemp), a horizontal line is provided at the upper limitof 140 MPa specified by the user. The optimum value for Melt Temperatureis shown as a vertical line at 304.45 degrees C. Thus, the user can seethe optimum value for the Melt Temperature as determined by theoptimization module 22 and the user can see that the Melt Temperaturemust remain above a certain value (approximately 290 degrees C.) to havethe Melt Pressure to Fill remain below the upper limit of 140 MPa. Theother plots in FIG. 13 may similarly depict the optimum value for afactor 134, a low limit 92 and a high limit 96.

FIG. 14 is a flowchart of a method for optimizing a product design in analternate embodiment of the invention. FIG. 14 is directed to theoptimization of a design for optical media, but it is understood thatthe invention is not limited to optical media. The term product is usedto generically refer to a variety of items for which a design may beoptimized.

As shown in FIG. 14, the process includes the steps described above withreference to FIG. 1 and further includes a media application step 200.The media application step 200 focuses the user on the manufacturing ofoptical media (e.g., compact discs) to enhance responses such as yield,throughput and cycle time. As shown in FIG. 14, the media applicationstep 200 allows the user to bypass steps 48-56 and proceed to the DOEsetup 58, if necessary, once the media application step 200 iscompleted. The media application step 200 is directed to applicationswhere the product being manufactured is optical media for use inproducts such as compact discs. Given the specificity of theapplication, the factors and responses are typically predetermined asdescribed herein. FIG. 15 is a block diagram of a system 11 in analternate embodiment of the invention. The system 11 is similar tosystem 10 in FIG. 2 and includes media application module 23. The mediaapplication module 23 may be implemented through a software applicationexecuted by a general-purpose computer. The media application module 23is coupled to other modules and the user interface through network 27.As described above with reference to FIG. 2, the network 27 may be theInternet and the user interface may be a user interface application(e.g., web browser).

If the user selects the media application step 200, the user defines themedia application through a user interface such as that shown in FIG.16. As shown in FIG. 16, the user interface allows the user to defineapplication parameters through an application setup region 210. Theapplication setup region 210 includes a process field 212 that definesthe type of process to be performed to generate the optical media.Exemplary processes include molding, metalization, bonding, curing,printing and mastering. A material field 214 defines the type ofmaterial to be used in forming the optical media. A format field 216defines the format for the optical media to be manufactured (e.g.,compact disc, digital video disc, etc.). A machine field 218 defines themachine used to form the optical media. A mold field 220 defines thetype of mold used. Because the optical media applications are governedby industry standards, the application parameters in the applicationsetup region 210 may be selected using a drop down menu in which theuser is presented with predetermined selections for each parameter.Alternatively, the user may enter parameters in each field that are notalready predefined.

Once the user has entered application parameters in application setupregion 210, the user can select the use of an archived transfer functionthrough icon 222. The use of archived transfer functions in transferfunction database 21 allows for rapid optimization without the need toperform design of experiments and regression. This greatly facilitatesthe optimization of designs. For the user to select a predeterminedtransfer function, a transfer function must have been derived for theset of parameters entered in application setup region 210. Requesting anarchived transfer function links the media application module 23 to thetransfer function database 21 and accesses an existing transfer functionfor use in optimization step 64.

The media application module 23 also presents the user with a list offactors 230 and responses 232. The factors 230 are directed to the setupof the machine for manufacturing the optical media based on theparameters entered in application setup region 210. As shown in FIG. 16,the low and high values for each machine setup factor are providedautomatically. The machine setup factors 230 are stored in a databasewhich is indexed by the parameters in application setup region 210.Exemplary machine setup factors for a molding process are shown in FIG.16. It is understood that other factors may be used. When the userenters the application parameters, the appropriate set of machine setupfactors and low/high limits are retrieved and presented to the user. Theuser may alter the factors 230 and the high and low limits if desired.The factors 230 also include a design of experiments indicator 234 whichthe user selects to request that a factor 230 be used in a subsequentdesign of experiments if a predetermined transfer function has not beenselected.

The responses 232 are directed to qualities of the optical media. Asshown in FIG. 16, the low and high values for each response 232 areprovided automatically. As discussed above, the qualities of certainoptical media may be standardized and the low and high limits forresponses 232 can be based on industry standards. The optical mediaquality responses 232 are stored in a database which is indexed by theparameters in application setup region 210. Exemplary optical mediaquality responses are shown in FIG. 16 for a molding process for acompact disc. It is understood that other responses may be useddepending on the application parameters specified by the user. The usermay alter the responses 232 and the high and low limits if desired. Theresponses 232 also include a design of experiments indicator 236 whichthe user selects to request that the response 232 be used in asubsequent design of experiments if a predetermined transfer functionhas not been selected.

As described above, an existing transfer function may be used ifavailable. If an existing transfer function exists, and the transferfunction meets the user's needs (i.e., the transfer function relates thefactors and responses that the user is interested in), then the processcan proceed to the optimization step 64 shown in FIG. 1. The user canspecify optimization conditions and the archived transfer function isused as described above. If the archived transfer function does notrelate the factors and responses of interest to the user, then flowproceeds to the design of experiments setup step 58 and a transferfunction is generated as described above with reference to steps 58, 60and 62. The user can then save the generated transfer function intransfer function database 21 for subsequent use.

Flow proceeds as described above through optimization step 64 andvisualization steps 66 and 68.

The optical media application module 23 allows the user to retrieverelevant machine setup factors and optical media responses based onapplication parameters set by the user. This helps focus the user onfactors and responses that are relevant to an application and eliminatesundue experimentation in deriving sets of useful factors and responses.This facilitates introduction of a new machine or manufacturing of a newformat. In addition, the availability of archived transfer functionsexpedites optimization. If a design of experiments requires experimentaldata to derive certain responses, the existence of a predeterminedtransfer function can greatly expedite the design process.

The system of FIG. 15 may be used to optimize designs for a variety ofproducts and is not limited to optical media. The user may be presentedwith icons for different applications and can select the desiredapplication. The transfer function database 21 contains transferfunctions for a variety of applications thereby enabling rapidoptimization with little or no experimentation needed. Of course, thesystem provides the ability to perform design of experiments andregression to generate transfer functions if needed.

As described above, the invention can be embodied in the form ofcomputer-implemented processes and apparatuses for practicing thoseprocesses. The present invention can also be embodied in the form ofcomputer program code containing instructions embodied in tangiblemedia, such as floppy diskettes, CD-ROMs, hard drives, or any othercomputer-readable storage medium, wherein, when the computer programcode is loaded into and executed by a computer, the computer becomes anapparatus for practicing the invention. The present invention can alsobe embodied in the form of computer program code, for example, whetherstored in a storage medium, loaded into and/or executed by a computer,or transmitted over some transmission medium, such as over electricalwiring or cabling, through fiber optics, or via electromagneticradiation, wherein, when the computer program code is loaded into andexecuted by a computer, the computer becomes an apparatus for practicingthe invention. When implemented on a general-purpose microprocessor, thecomputer program code segments configure the microprocessor to createspecific logic circuits.

While the invention has been described with reference to exemplaryembodiments, it will be understood by those skilled in the art thatvarious changes may be made and equivalents may be substituted forelements thereof without departing from the scope of the invention. Inaddition, many modifications may be made to adapt a particular situationor material to the teachings of the invention without departing from theessential scope thereof. Therefore, it is intended that the inventionnot be limited to the particular embodiments disclosed for carrying outthis invention, but that the invention will include all embodimentsfalling within the scope of the appended claims.

What is claimed is:
 1. A method for optimizing a product design, themethod comprising: specifying a plurality of application parameters forthe product design; obtaining a plurality of predetermined factors and auser-defined factor in response to said plurality of applicationparameters; obtaining a plurality of predetermined responses and auser-defined response in response to said plurality or applicationparameters; obtaining a transfer function relating at least one factorto at least one response; optimizing said transfer function in responseto predetermined optimization criteria and user-defined optimizationcriteria to generate an optimized factor and an optimized response; anddisplaying said optimized factor and said optimized response.
 2. Themethod of claim 1 wherein: said specifying a plurality of applicationparameters includes specifying a material to be used in forming theproduct.
 3. The method of claim 1 wherein: said specifying a pluralityof application parameters includes specifying a format for the product.4. The method of claim 1 wherein: said specifying a plurality ofapplication parameters includes specifying a machine to be used informing the product.
 5. The method of claim 1 wherein: said specifying aplurality of application parameters includes specifying a mold to beused in forming the product.
 6. The method of claim 1 wherein: saidobtaining a transfer function includes retrieving an archived transferfunction.
 7. The method of claim 1 wherein said obtaining a transferfunction includes: performing a design of experiments routine togenerate design of experiments data relating at least one factor to atleast one response; and performing regression to generate a transferfunction in response to said design of experiments data.
 8. The methodof claim 1 wherein: said product is optical media.
 9. The method ofclaim 1 wherein: said optimizing said transfer function includesassigning a weight to at least one of said predetermined responses anduser-defined responses.
 10. A system for optimizing a product design,the system comprising: a user interface for specifying a plurality ofapplication parameters for the product design; an application module forobtaining a plurality of predetermined factors and a user-defined factorin response to said plurality of application parameters; saidapplication module obtaining a plurality of predetermined responses anda user-defined response in response to said plurality of applicationparameters; a transfer function database containing a transfer functionrelating at least one factor to at least one response; an optimizationmodules for optimizing said transfer function in response topredetermined optimization criteria and user-defined optimizationcriteria to generate an optimized factor and an optimized response; anda visualization module for displaying said optimized factor and saidoptimized response.
 11. The system of claim 10 wherein: said applicationparameters include a material to be used in forming the product.
 12. Thesystem of claim 10 wherein: said application parameters include a formatfor the product.
 13. The system of claim 10 wherein: said applicationparameters include a machine to be used in forming the product.
 14. Thesystem of claim 10 wherein: said application parameters include a moldto be used in forming the product.
 15. The system of claim 10 wherein:said product is optical media.
 16. The system of claim 10 wherein: saidoptimizing said transfer function includes assigning a weight to atleast one of said predetermined responses and user-defined responses.17. A storage medium encoded with machine-readable computer program codefor optimizing a product design, the storage medium includinginstructions for causing a computer to implement a method comprising:receiving a plurality of application parameters for the product design;obtaining a plurality of predetermined factors and a user-defined factorin response to said plurality of application parameters; obtaining aplurality of predetermined responses and a user-defined response inresponse to said plurality of application parameters; obtaining atransfer function relating at least one factor to at least one response;optimizing said transfer function in response to predeterminedoptimization criteria and user-defined optimization criteria to generatean optimized factor and an optimized response; and displaying saidoptimized factor and said optimized response.
 18. The storage medium ofclaim 17 wherein: said plurality of application parameters include amaterial to be used in forming the product.
 19. The storage medium ofclaim 17 wherein: said plurality of application parameters include aformat for the product.
 20. The storage medium of claim 17 wherein: saidplurality of application parameters include a machine to be used informing the product.
 21. The storage medium of claim 17 wherein: saidplurality of application parameters include a mold to be used in formingthe product.
 22. The storage medium of claim 17 wherein: said obtaininga transfer function includes retrieving an archived transfer function.23. The storage medium of claim 17 wherein said obtaining a transferfunction includes: performing a design of experiments routine togenerate design of experiments data relating at least one factor to atleast one response; and performing regression to generate a transferfunction in response to said design of experiments data.
 24. The storagemedium of claim 17 wherein: said product is optical media.
 25. Thestorage medium of claim 17 wherein: said optimizing said transferfunction includes assigning a weight to at least one of saidpredetermined responses and user-defined responses.